Microseismic event data is sometimes acquired as part of the hydraulic fracturing treatment of a well that has been drilled for the purpose of developing hydrocarbon reserves. The data is acquired from low amplitude (micro) seismic events that are associated with fractures that have been induced by hydraulic fracturing. The purpose of the hydraulic fracturing is to induce an artificial fracture into the subsurface, by injecting high pressured fluids and proppants into the rock matrix, in order to enhance the productivity of the reservoir for hydrocarbons.
The microseismic event locations are commonly monitored in real-time and the locations of events shown in a three-dimensional (3D) view may be validated as they occur. They are also available for analysis after the conclusion of the hydraulic fracturing treatment and are thus, available to be compared to the results of other wells in the area. The microseismic events usually occur along or near subsurface fractures that may be either induced or preexisting natural fractures that have been reopened by the hydraulic fracturing treatment. The orientation of the fractures is strongly influenced by the present-day stress regime and also by the presence of fracture systems that were generated at various times in the past when the stress orientation was different from that at the present.
Each separate and distinct microseismic event that is detected and analyzed is the result of a downhole fracture, which has an orientation, magnitude, location and other attributes that can be extracted from a tiltmeter or seismic sensor data. The fracture may be characterized with other parameters such as length, width, height, and pressure, for example. There is a location uncertainty associated with each microseismic event. This uncertainty is different in the x-y direction than it is in the vertical (z) depth domain. The location uncertainty of each event may be represented by a prolate spheroid.
In some cases, there is an obvious orientation and spacing of microseismic events that follows the classical bi-wing fracture concepts that are often used in mathematical depictions of fracture analysis. In other cases, a dense data cloud, which represents the 3D volume that encompasses all of the microseismic datapoints, is evidence of a complex fracture pattern of induced or reactivated fractures. In these cases, the analysis of the microseismic data becomes very subjective and interpretive. Even in these cases, there are patterns within the data cloud that may be representative of the fracture patterns that are present in the subsurface.
The stress field today may be different from the one at the time of the original fracture creation. The present-day orientation of the induced hydraulic fractures is strongly influenced by the stress state in the subsurface. There is always some degree of stress anisotropy between the vertical stress and the two horizontal stresses. The greater the anisotropy, the more planar the fractures that are induced by hydraulic fracturing stimulation and the more they will fit the traditional bi-wing model. The greater the permeability of the rock, the more planar the fractures will be. The more isotropic the stress regime, the more the fractures can be easily deflected by discontinuities in the rock and can create a complex fracture network.
Currently, there are several fracture characterization techniques that have been used to try and identify the orientation, dip and spacing of induced and natural fractures.
In one technique, the overall data cloud of microseismic datapoints is identified to build a stimulated reservoir volume, SRV or estimated stimulated volume, ESV. The information is inferred to be a measure of the amount of rock that has been stimulated by the fluids and proppants. Only a small portion of the energy that is pumped into the ground, however, is ever received at the surface as detectable microseismic events. In fact, there are many microseismic events that are below the detection range of the instrumentation. Therefore, the use of the data cloud dimensions is only a guide and the results of such fracture characterization techniques may not be valid. Interpreting the fracture pattern based on a pattern of microseismic event data is another technique that is described in Optimizing Horizontal Completion Technologies in the Barnett Shale Using Microseismic Fracture Mapping by M. K. Fisher, et al. Surface seismic data (both active and passive) and analysis is yet another technique for fracture characterization. In some cases there may be a difference in the lateral variation of velocity that is related to the presence of fractures in the subsurface. The velocity is faster in some directions than it is in other directions. Converted waves (P to S for example) have also been used as fracture characterization techniques.
There are also several different fracture characterization techniques that are able to mathematically associate the microseismic event data with a model of the subsurface and produce a discrete fracture network (DFN) or a set of probable fracture characterizations such as, for example, the techniques described in U.S. Patent Application Publication Nos. 2010/0307755 and 0211/0029291.
An automated fracture characterization technique was proposed in Quantitative interpretation of Major Planes From Microseismic Event Locations with Application in Production Prediction by M. J. Williams, et al. to numerically analyze the data cloud and to identify the major planes from the microseismic datapoints for use in reservoir modeling. This technique approaches the problem of identifying the fracture trends from a probabilistic framework, however, only a single fracture orientation is identified.